The increased microseismicity rates resulting
from the San Simeon M6.5 and Parkfield M6 events
and the increased interest in even smaller events
in the SAFOD target zone have required new thinking on
how to detect and catalog microearthquakes recorded by the HRSN.
One action taken to help address this problem has been
to integrate HRSN data streams into the NCSN event
detection and automated cataloging process (described below).
This approach has been successful at discriminating small events in the
local Parkfield area from other types of event detections and for
providing automated locations of a significantly increased number of
small events in the local area (approx. double that of the NCSN network
alone). However, the rate of local events from the HRSN sensitized
NCSN catalog is still only catching about 1/2 the number of local
events previously cataloged by the HRSN, and waveforms for the
small events are not typically made available. In addition, unlike
the previous HRSN catalog, the additional events added by the NCSN-HRSN
integration are not reviewed by an analyst, nor do they generally have
magnitude determinations associated with them. In some cases, the
selection rules used for the integrated catalog also result in
exclusion of events that are otherwise included by the NCSN.

These limitations severely hamper efforts relying on similar
and characteristically repeating microearthquakes. They
also reduce the effectiveness of research relying on numerous
very small magnitude events in the SAFOD zone (e.g. for
targeting the SAFOD targets).
To help overcome these limitations,
we have continued our efforts to develop an automated
similar event cataloging scheme based on cross-correlation
and pattern scanning of the continuous HRSN data now being
archived. The method uses a small number of reference events
whose waveforms, picks, locations, and magnitudes have been
accurately determined, and it automatically detects, picks, locates
and determines magnitudes for events similar to the reference
event to the level of accuracy and precision that only relative
event analysis can bring.

The similar event detection is also remarkably insensitive to the
magnitude of the reference event used, allowing similar events
ranging over several magnitude units to be fully cataloged using
a single reference event. It also does a remarkably good job
even when seismic energy from multiple events is superposed.
Once a cluster of similar
events has been cataloged, it is a relatively straightforward process
to identify characteristically repeating microearthquake sequences
within the cluster (frequently a single similar event "cluster" will
contain several sequences of repeating events).

Application of the method using two of the SAFOD target events as
references is illustrated in Figure 3.14. One reference
event is a member of the so-called Hawaii sequence (HI), and one is from the
San Francisco sequence (SF), and their magnitudes
are 2.1 and 1.8 (respectively). These events were scanned
through 5 years of continuous data, and 110 other events occurring
within the target region were identified and fully cataloged to high
precision. Their magnitudes ranged down to magnitude -1.4 Ml, and
in addition to the SAFOD target sequence from which the reference was
derived, several other repeating sequences within the 150m zone were
also identified (5 of which had not previously been known to exist).

This high level of precision and low magnitude completeness has already
proven useful to SAFOD for helping to delineate and
constrain the active fault structure in the target zone. It has also
proven vital for helping to resolve a long-standing debate in the
seismologic community regarding the stress-drop scaling issues
(Dreger et al., 2007).

The automated cataloging procedure for similar events is still
being refined to capture even smaller events and
events over a larger area, as well as for increased processing speed.
Eventually, a composite catalog of similar event groups from throughout
the HRSN coverage zone is planned.

The approach also holds promise in other applications where
automated and precise monitoring of bursts of seismic activity
to very low magnitudes is desirable (e.g. in aftershock zones
or in volcanic regions) or where automated updates of
preexisting repeating sequences and their associated deep slip
estimates are desired.